Hi, my name is Sophie Tao, I am an alumn at the University of Washington, with majoring in Electrical and Computer Engineering, I’m happy to share the progress I have been able to make over the last six weeks on my GSoC 2025 project, WildBerryEye, mentored by Carlos Isaac Espinosa.

Project Overview

WildBerryEye is an open-source initiative to support ecological monitoring of pollinators such as bees and hummingbirds using edge computing and computer vision. The project leverages a Raspberry Pi and YOLO for object detection and aims to provide an accessible, responsive, and real-time web interface for researchers, ecologists, and citizen scientists.

This project specifically focuses on building the frontend and backend infrastructure for WildBerryEye’s user interface, enabling:

• Real-time pollinator detection preview
  • Real-time image capture
  • Real time video capture
• Responsive, User-friendly UI
• Object detection
• Researcher-friendly configuration and usability

Progress So Far

✅ Phase 1: Setup

• Frontend: Completed React + TypeScript project initialization with routing and base components. Pages include:

  • Home page (with image preview)
  • Dashboard page (pollinator image & video)

• Backend: Flask server initialized with modular structure. Basic API endpoints stubbed as per the proposal.

✅ Phase 2: Core Features

• Real-Time Communication: Frontend successfully receives image stream using WebSocket.

• UI Components:

  • Implemented image carousel preview on homepage.
  • Image Capture (Image download)
  • Video Capture (Video Preview, Video Recording)
  • Sidebar-based navigation and page structure fully integrated.

• API Development:

  • Implemented core endpoints such as /home, and/dashboard routes.
  • Backend handlers structured for image and video capture.

Challenges Encountered

⚠️ Real-time Image Testing: Lack of consistent live camera input made local testing inconsistent.
⚠️ Allocate the camera module for both capture image and capture video.
⚠️ Obtain the proper format of the video.

Next Steps

• Enable more features for video capture
• Integrated with Machine Learning Model
• Conduct at least one usability test (self + external user) and incorporate feedback.
• Final Testing & Docs

Summary

At this midterm stage, the WildBerryEye UI project is on track with core milestones completed, including real-time communication, component setup, and backend API structure. The remaining work focuses on refinement, visualizations, testing, and documentation to ensure a polished final product by the end of GSoC 2025.